WHO: LifeExpectancy regression: WHO_2016
bdanalytics
Date: (Wed) Apr 13, 2016
Introduction:
Data: Source: Training: https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/WHO.csv New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<pointer>"; if url specifies a zip file, name = "<filename>"
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://d37djvu3ytnwxt.cloudfront.net/asset-v1:MITx+15.071x_3+1T2016+type@asset+block/WHO.csv")
glbObsNewFile <- #list(url = "<obsNewFileName>") # default OR
list(splitSpecs = list(method = "copy" #select from c(NULL, "condition", "sample", "copy")
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
)
)
glbInpMerge <- NULL #: default
# list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated
glb_is_separate_newobs_dataset <- FALSE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # FALSE not supported - use "copy" for glbObsNewFile$splitSpecs$method
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- NULL # or TRUE or FALSE
glb_rsp_var_raw <- "LifeExpectancy"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "LifeExpectancy.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL
# function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
# }
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
#print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- NULL
# function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
# }
#print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "Country" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- NULL # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category & work each one in
#
# Too many unique factor values
, "Country.fctr"
# Too many missing
, "PrimarySchoolEnrollmentMale", "PrimarySchoolEnrollmentFemale"
, "LiteracyRate"
, "GNI"
# Impute generates "singular" error
#, "FertilityRate"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
glbFeatsDerive[[".pos.y"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE,
# last.ctg = TRUE, poly.ctg = TRUE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- TRUE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsout_df) {
# require(tidyr)
# obsout_df <- obsout_df %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsout_df %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsout_df %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsout_df,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsout_df) {
# }
)
#obsout_df <- savobsout_df
# glbObsOut$mapFn <- function(obsout_df) {
# txfout_df <- dplyr::select(obsout_df, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsout_df <- glbObsOut$mapFn(obsout_df); print(head(obsout_df))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
glbObsOut$vars[["Probability1"]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "<scriptName>_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk
#mysavChunk(glbOut$pfx, glbChunks[["last"]])
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("<scriptName>_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 5.756 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/WHO.csv..."
## [1] "dimensions of data in ./data/WHO.csv: 194 rows x 13 cols"
## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 2 Albania Europe 3162 21.33 14.93
## 3 Algeria Africa 38482 27.42 7.17
## 4 Andorra Europe 78 15.20 22.86
## 5 Angola Africa 20821 47.58 3.84
## 6 Antigua and Barbuda Americas 89 25.96 12.35
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 2 1.75 74 16.7 96.39
## 3 2.83 73 20.0 98.99
## 4 NA 82 3.2 75.49
## 5 6.10 51 163.5 48.38
## 6 2.12 75 9.9 196.41
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 2 NA 8820 NA
## 3 NA 8310 98.2
## 4 NA NA 78.4
## 5 70.1 5230 93.1
## 6 99.0 17900 91.1
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 3 96.4
## 4 79.4
## 5 78.2
## 6 84.5
## Country Region Population Under15 Over60
## 7 Argentina Americas 41087 24.42 14.97
## 29 Cambodia Western Pacific 14865 31.23 7.67
## 99 Lithuania Europe 3028 15.13 20.57
## 140 Republic of Moldova Europe 3514 16.52 16.72
## 141 Romania Europe 21755 15.05 20.66
## 191 Viet Nam Western Pacific 90796 22.87 9.32
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 7 2.20 76 14.2 134.92
## 29 2.93 65 39.7 96.17
## 99 1.49 74 5.4 151.30
## 140 1.47 71 17.6 104.80
## 141 1.39 74 12.2 109.16
## 191 1.79 75 23.0 143.39
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 7 97.8 17130 NA
## 29 NA 2230 96.4
## 99 99.7 19640 95.6
## 140 98.5 3640 90.1
## 141 97.7 15120 87.9
## 191 93.2 3250 NA
## PrimarySchoolEnrollmentFemale
## 7 NA
## 29 95.4
## 99 95.8
## 140 90.1
## 141 87.3
## 191 NA
## Country Region Population
## 189 Vanuatu Western Pacific 247
## 190 Venezuela (Bolivarian Republic of) Americas 29955
## 191 Viet Nam Western Pacific 90796
## 192 Yemen Eastern Mediterranean 23852
## 193 Zambia Africa 14075
## 194 Zimbabwe Africa 13724
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 189 37.37 6.02 3.46 72 17.9
## 190 28.84 9.17 2.44 75 15.3
## 191 22.87 9.32 1.79 75 23.0
## 192 40.72 4.54 4.35 64 60.0
## 193 46.73 3.95 5.77 55 88.5
## 194 40.24 5.68 3.64 54 89.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 189 55.76 82.6 4330 NA
## 190 97.78 NA 12430 94.7
## 191 143.39 93.2 3250 NA
## 192 47.05 63.9 2170 85.5
## 193 60.59 71.2 1490 91.4
## 194 72.13 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 189 NA
## 190 95.1
## 191 NA
## 192 70.5
## 193 93.9
## 194 NA
## 'data.frame': 194 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
## $ Population : int 29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
## $ Under15 : num 47.4 21.3 27.4 15.2 47.6 ...
## $ Over60 : num 3.82 14.93 7.17 22.86 3.84 ...
## $ FertilityRate : num 5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
## $ LifeExpectancy : int 60 74 73 82 51 75 76 71 82 81 ...
## $ ChildMortality : num 98.5 16.7 20 3.2 163.5 ...
## $ CellularSubscribers : num 54.3 96.4 99 75.5 48.4 ...
## $ LiteracyRate : num NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
## $ GNI : num 1140 8820 8310 NA 5230 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...
## - attr(*, "comment")= chr "glbObsTrn"
## NULL## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 2 Albania Europe 3162 21.33 14.93
## 3 Algeria Africa 38482 27.42 7.17
## 4 Andorra Europe 78 15.20 22.86
## 5 Angola Africa 20821 47.58 3.84
## 6 Antigua and Barbuda Americas 89 25.96 12.35
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 2 1.75 74 16.7 96.39
## 3 2.83 73 20.0 98.99
## 4 NA 82 3.2 75.49
## 5 6.10 51 163.5 48.38
## 6 2.12 75 9.9 196.41
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 2 NA 8820 NA
## 3 NA 8310 98.2
## 4 NA NA 78.4
## 5 70.1 5230 93.1
## 6 99.0 17900 91.1
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 3 96.4
## 4 79.4
## 5 78.2
## 6 84.5
## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 34 Chad Africa 12448 48.52 3.80
## 75 Honduras Americas 7936 35.72 6.41
## 76 Hungary Europe 9976 14.62 23.41
## 77 Iceland Europe 326 20.71 17.62
## 89 Kenya Africa 43178 42.37 4.25
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 34 6.49 51 149.8 31.80
## 75 3.10 74 22.9 103.97
## 76 1.38 75 6.2 117.30
## 77 2.11 82 2.3 106.08
## 89 4.54 60 72.9 67.49
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 34 34.5 1360 NA
## 75 84.8 3820 94.8
## 76 99.0 20310 97.8
## 77 NA 31020 98.8
## 89 87.4 1710 NA
## PrimarySchoolEnrollmentFemale
## 1 NA
## 34 NA
## 75 97.0
## 76 98.3
## 77 99.2
## 89 NA
## Country Region Population
## 189 Vanuatu Western Pacific 247
## 190 Venezuela (Bolivarian Republic of) Americas 29955
## 191 Viet Nam Western Pacific 90796
## 192 Yemen Eastern Mediterranean 23852
## 193 Zambia Africa 14075
## 194 Zimbabwe Africa 13724
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 189 37.37 6.02 3.46 72 17.9
## 190 28.84 9.17 2.44 75 15.3
## 191 22.87 9.32 1.79 75 23.0
## 192 40.72 4.54 4.35 64 60.0
## 193 46.73 3.95 5.77 55 88.5
## 194 40.24 5.68 3.64 54 89.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 189 55.76 82.6 4330 NA
## 190 97.78 NA 12430 94.7
## 191 143.39 93.2 3250 NA
## 192 47.05 63.9 2170 85.5
## 193 60.59 71.2 1490 91.4
## 194 72.13 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 189 NA
## 190 95.1
## 191 NA
## 192 70.5
## 193 93.9
## 194 NA
## 'data.frame': 194 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
## $ Population : int 29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
## $ Under15 : num 47.4 21.3 27.4 15.2 47.6 ...
## $ Over60 : num 3.82 14.93 7.17 22.86 3.84 ...
## $ FertilityRate : num 5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
## $ LifeExpectancy : int 60 74 73 82 51 75 76 71 82 81 ...
## $ ChildMortality : num 98.5 16.7 20 3.2 163.5 ...
## $ CellularSubscribers : num 54.3 96.4 99 75.5 48.4 ...
## $ LiteracyRate : num NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
## $ GNI : num 1140 8820 8310 NA 5230 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...
## - attr(*, "comment")= chr "glbObsNew"
## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 2 Albania Europe 3162 21.33 14.93
## 3 Algeria Africa 38482 27.42 7.17
## 4 Andorra Europe 78 15.20 22.86
## 5 Angola Africa 20821 47.58 3.84
## 6 Antigua and Barbuda Americas 89 25.96 12.35
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 2 1.75 74 16.7 96.39
## 3 2.83 73 20.0 98.99
## 4 NA 82 3.2 75.49
## 5 6.10 51 163.5 48.38
## 6 2.12 75 9.9 196.41
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 2 NA 8820 NA
## 3 NA 8310 98.2
## 4 NA NA 78.4
## 5 70.1 5230 93.1
## 6 99.0 17900 91.1
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 3 96.4
## 4 79.4
## 5 78.2
## 6 84.5
## Country Region Population Under15 Over60
## 63 Gabon Africa 1633 38.49 7.38
## 88 Kazakhstan Europe 16271 25.46 10.04
## 122 New Zealand Western Pacific 4460 20.26 19.01
## 135 Philippines Western Pacific 96707 34.53 6.21
## 185 United Republic of Tanzania Africa 47783 44.85 4.89
## 194 Zimbabwe Africa 13724 40.24 5.68
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 63 4.18 62 62.0 117.32
## 88 2.52 67 18.7 155.74
## 122 2.10 81 5.7 109.19
## 135 3.11 69 29.8 99.30
## 185 5.36 59 54.0 55.53
## 194 3.64 54 89.8 72.13
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 63 88.4 13740 NA
## 88 99.7 11250 NA
## 122 NA NA 99.3
## 135 NA 4140 NA
## 185 73.2 1500 NA
## 194 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 63 NA
## 88 NA
## 122 99.6
## 135 NA
## 185 NA
## 194 NA
## Country Region Population
## 189 Vanuatu Western Pacific 247
## 190 Venezuela (Bolivarian Republic of) Americas 29955
## 191 Viet Nam Western Pacific 90796
## 192 Yemen Eastern Mediterranean 23852
## 193 Zambia Africa 14075
## 194 Zimbabwe Africa 13724
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 189 37.37 6.02 3.46 72 17.9
## 190 28.84 9.17 2.44 75 15.3
## 191 22.87 9.32 1.79 75 23.0
## 192 40.72 4.54 4.35 64 60.0
## 193 46.73 3.95 5.77 55 88.5
## 194 40.24 5.68 3.64 54 89.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 189 55.76 82.6 4330 NA
## 190 97.78 NA 12430 94.7
## 191 143.39 93.2 3250 NA
## 192 47.05 63.9 2170 85.5
## 193 60.59 71.2 1490 91.4
## 194 72.13 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 189 NA
## 190 95.1
## 191 NA
## 192 70.5
## 193 93.9
## 194 NA
## 'data.frame': 194 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
## $ Population : int 29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
## $ Under15 : num 47.4 21.3 27.4 15.2 47.6 ...
## $ Over60 : num 3.82 14.93 7.17 22.86 3.84 ...
## $ FertilityRate : num 5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
## $ LifeExpectancy : int 60 74 73 82 51 75 76 71 82 81 ...
## $ ChildMortality : num 98.5 16.7 20 3.2 163.5 ...
## $ CellularSubscribers : num 54.3 96.4 99 75.5 48.4 ...
## $ LiteracyRate : num NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
## $ GNI : num 1140 8820 8310 NA 5230 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...
## - attr(*, "comment")= chr "glbObsTrn"## Warning: glbObsTrn same as glbObsAll## Warning: glbObsNew same as glbObsAll## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: .pos.y..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## LifeExpectancy.cut.fctr .src .n
## 1 (71,83] Test 107
## 2 (71,83] Train 107
## 3 (59,71] Test 54
## 4 (59,71] Train 54
## 5 (47,59] Test 33
## 6 (47,59] Train 33
## LifeExpectancy.cut.fctr .src .n
## 1 (71,83] Test 107
## 2 (71,83] Train 107
## 3 (59,71] Test 54
## 4 (59,71] Train 54
## 5 (47,59] Test 33
## 6 (47,59] Train 33## Loading required package: RColorBrewer
## .src .n
## 1 Test 194
## 2 Train 194## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Skipping duplicates check since glbObsNewFile$splitSpecs$method == 'copy'"## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 5.756 7.591 1.835
## 2 inspect.data 2 0 0 7.591 NA NAStep 2.0: inspect data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## [1] "numeric data missing in glbObsAll: "
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Country Region
## 0 0
## Warning: Removed 22 rows containing non-finite values (stat_smooth).
## Warning: Removed 22 rows containing non-finite values (stat_smooth).## Warning: Removed 22 rows containing missing values (geom_point).
## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
## NULL## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 7.591 14.998 7.407
## 3 scrub.data 2 1 1 14.998 NA NAStep 2.1: scrub data
## [1] "numeric data missing in glbObsAll: "
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Country Region
## 0 0## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 14.998 18.144 3.146
## 4 transform.data 2 2 2 18.144 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 18.144 18.183 0.039
## 5 extract.features 3 0 0 18.183 NA NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 18.183
## 6 extract.features.datetime 3 1 1 18.204
## end elapsed
## 5 18.203 0.02
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 18.23
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 18.204
## 7 extract.features.image 3 2 2 18.239
## end elapsed
## 6 18.239 0.035
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 18.272 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 18.272
## 2 extract.features.image.end 2 0 0 18.281
## end elapsed
## 1 18.28 0.009
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 18.272
## 2 extract.features.image.end 2 0 0 18.281
## end elapsed
## 1 18.28 0.009
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 18.239 18.29
## 8 extract.features.price 3 3 3 18.291 NA
## elapsed
## 7 0.051
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 18.317 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 18.291 18.326
## 9 extract.features.text 3 4 4 18.327 NA
## elapsed
## 8 0.035
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 18.367 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 18.327 18.376
## 10 extract.features.string 3 5 5 18.376 NA
## elapsed
## 9 0.049
## 10 NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn end
## 1 extract.features.string.bgn 1 0 0 18.424 NA
## elapsed
## 1 NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 18.424 18.434 0.01
## 2 0 18.435 NA NA## Country Region .src
## "Country" "Region" ".src"## Warning: Creating factors of string variable: Country: # of unique values:
## 194## Warning: Creating factors of string variable: Region: # of unique values: 6## label step_major step_minor label_minor bgn end
## 10 extract.features.string 3 5 5 18.376 18.452
## 11 extract.features.end 3 6 6 18.453 NA
## elapsed
## 10 0.076
## 11 NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 18.453 19.321
## 12 manage.missing.data 4 0 0 19.321 NA
## elapsed
## 11 0.868
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in glbObsAll: "
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Country Region
## 0 0## [1] "Missing data for numerics:"
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186## Loading required package: mice## Loading required package: Rcpp## mice 2.25 2015-11-09## [1] "Summary before imputation: "
## Population Under15 Over60 FertilityRate
## Min. : 1 Min. :13.12 Min. : 0.81 Min. :1.260
## 1st Qu.: 1664 1st Qu.:18.64 1st Qu.: 5.18 1st Qu.:1.833
## Median : 7790 Median :28.65 Median : 8.53 Median :2.400
## Mean : 36360 Mean :28.73 Mean :11.16 Mean :2.941
## 3rd Qu.: 24763 3rd Qu.:37.88 3rd Qu.:16.72 3rd Qu.:3.908
## Max. :1390000 Max. :49.99 Max. :31.92 Max. :7.580
## NA's :22
## ChildMortality CellularSubscribers Region.fctr
## Min. : 2.20 Min. : 2.57 Europe :106
## 1st Qu.: 8.40 1st Qu.: 63.57 Africa : 92
## Median : 18.60 Median : 97.75 Americas : 70
## Mean : 36.15 Mean : 93.64 Eastern Mediterranean: 44
## 3rd Qu.: 56.30 3rd Qu.:120.81 South-East Asia : 22
## Max. :181.60 Max. :196.41 Western Pacific : 54
## NA's :20
##
## iter imp variable
## 1 1 FertilityRate CellularSubscribers
## 1 2 FertilityRate CellularSubscribers
## 1 3 FertilityRate CellularSubscribers
## 1 4 FertilityRate CellularSubscribers
## 1 5 FertilityRate CellularSubscribers
## 2 1 FertilityRate CellularSubscribers
## 2 2 FertilityRate CellularSubscribers
## 2 3 FertilityRate CellularSubscribers
## 2 4 FertilityRate CellularSubscribers
## 2 5 FertilityRate CellularSubscribers
## 3 1 FertilityRate CellularSubscribers
## 3 2 FertilityRate CellularSubscribers
## 3 3 FertilityRate CellularSubscribers
## 3 4 FertilityRate CellularSubscribers
## 3 5 FertilityRate CellularSubscribers
## 4 1 FertilityRate CellularSubscribers
## 4 2 FertilityRate CellularSubscribers
## 4 3 FertilityRate CellularSubscribers
## 4 4 FertilityRate CellularSubscribers
## 4 5 FertilityRate CellularSubscribers
## 5 1 FertilityRate CellularSubscribers
## 5 2 FertilityRate CellularSubscribers
## 5 3 FertilityRate CellularSubscribers
## 5 4 FertilityRate CellularSubscribers
## 5 5 FertilityRate CellularSubscribers
## Population Under15 Over60 FertilityRate
## Min. : 1 Min. :13.12 Min. : 0.81 Min. :1.260
## 1st Qu.: 1664 1st Qu.:18.64 1st Qu.: 5.18 1st Qu.:1.837
## Median : 7790 Median :28.65 Median : 8.53 Median :2.380
## Mean : 36360 Mean :28.73 Mean :11.16 Mean :2.901
## 3rd Qu.: 24763 3rd Qu.:37.88 3rd Qu.:16.72 3rd Qu.:3.810
## Max. :1390000 Max. :49.99 Max. :31.92 Max. :7.580
## ChildMortality CellularSubscribers Region.fctr
## Min. : 2.20 Min. : 2.57 Europe :106
## 1st Qu.: 8.40 1st Qu.: 63.70 Africa : 92
## Median : 18.60 Median : 97.75 Americas : 70
## Mean : 36.15 Mean : 94.30 Eastern Mediterranean: 44
## 3rd Qu.: 56.30 3rd Qu.:122.98 South-East Asia : 22
## Max. :181.60 Max. :196.41 Western Pacific : 54## [1] "numeric data missing in glbObsAll: "
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Country Region
## 0 0## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 19.321 20.206
## 13 cluster.data 5 0 0 20.206 NA
## elapsed
## 12 0.885
## 13 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn end
## 13 cluster.data 5 0 0 20.206 20.252
## 14 partition.data.training 6 0 0 20.253 NA
## elapsed
## 13 0.047
## 14 NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "Newdata contains non-NA data for LifeExpectancy; setting OOB to Newdata"## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 0.01 secs"## .category .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1 .dummy 194 194 194 1 1
## .freqRatio.Tst
## 1 1## [1] "glbObsAll: "## [1] 388 23## [1] "glbObsTrn: "## [1] 194 23## [1] "glbObsFit: "## [1] 194 22## [1] "glbObsOOB: "## [1] 194 22## [1] "glbObsNew: "## [1] 194 22## [1] "partition.data.training chunk: teardown: elapsed: 0.10 secs"## label step_major step_minor label_minor bgn end
## 14 partition.data.training 6 0 0 20.253 20.411
## 15 select.features 7 0 0 20.412 NA
## elapsed
## 14 0.158
## 15 NAStep 7.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## Loading required package: reshape2## [1] "cor(FertilityRate.nonNA, Under15)=0.9329"
## [1] "cor(LifeExpectancy, FertilityRate.nonNA)=-0.8348"
## [1] "cor(LifeExpectancy, Under15)=-0.8365"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified FertilityRate.nonNA as highly correlated with
## Under15## [1] "cor(Over60, Under15)=-0.8294"
## [1] "cor(LifeExpectancy, Over60)=0.6881"
## [1] "cor(LifeExpectancy, Under15)=-0.8365"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Over60 as highly correlated with Under15## [1] "cor(ChildMortality, Under15)=0.8151"
## [1] "cor(LifeExpectancy, ChildMortality)=-0.9246"
## [1] "cor(LifeExpectancy, Under15)=-0.8365"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Under15 as highly correlated with ChildMortality## cor.y exclude.as.feat cor.y.abs
## LiteracyRate 0.725924354 1 0.725924354
## PrimarySchoolEnrollmentFemale 0.711333918 1 0.711333918
## Over60 0.688129029 0 0.688129029
## GNI 0.665785621 1 0.665785621
## PrimarySchoolEnrollmentMale 0.630538168 1 0.630538168
## CellularSubscribers 0.623250842 1 0.623250842
## CellularSubscribers.nonNA 0.610735054 0 0.610735054
## .rnorm 0.081812822 0 0.081812822
## Population 0.016277014 0 0.016277014
## Region.fctr 0.002056609 0 0.002056609
## .pos -0.020262123 0 0.020262123
## .pos.y -0.020262123 0 0.020262123
## Country.fctr -0.026371656 1 0.026371656
## FertilityRate.nonNA -0.834846352 0 0.834846352
## Under15 -0.836466728 0 0.836466728
## FertilityRate -0.839839511 1 0.839839511
## ChildMortality -0.924563674 0 0.924563674
## .category NA 1 NA
## cor.high.X freqRatio percentUnique
## LiteracyRate <NA> 2.000000 40.7216495
## PrimarySchoolEnrollmentFemale <NA> 1.000000 39.6907216
## Over60 Under15 1.333333 89.6907216
## GNI <NA> 1.000000 80.9278351
## PrimarySchoolEnrollmentMale <NA> 1.250000 38.6597938
## CellularSubscribers <NA> 1.000000 92.7835052
## CellularSubscribers.nonNA <NA> 1.500000 92.7835052
## .rnorm <NA> 1.000000 100.0000000
## Population <NA> 1.000000 98.4536082
## Region.fctr <NA> 1.152174 3.0927835
## .pos <NA> 1.000000 100.0000000
## .pos.y <NA> 1.000000 100.0000000
## Country.fctr <NA> 1.000000 100.0000000
## FertilityRate.nonNA Under15 1.000000 73.7113402
## Under15 ChildMortality 1.333333 92.2680412
## FertilityRate <NA> 1.250000 73.7113402
## ChildMortality <NA> 1.333333 87.6288660
## .category <NA> 0.000000 0.5154639
## zeroVar nzv is.cor.y.abs.low
## LiteracyRate FALSE FALSE FALSE
## PrimarySchoolEnrollmentFemale FALSE FALSE FALSE
## Over60 FALSE FALSE FALSE
## GNI FALSE FALSE FALSE
## PrimarySchoolEnrollmentMale FALSE FALSE FALSE
## CellularSubscribers FALSE FALSE FALSE
## CellularSubscribers.nonNA FALSE FALSE FALSE
## .rnorm FALSE FALSE FALSE
## Population FALSE FALSE TRUE
## Region.fctr FALSE FALSE TRUE
## .pos FALSE FALSE TRUE
## .pos.y FALSE FALSE TRUE
## Country.fctr FALSE FALSE TRUE
## FertilityRate.nonNA FALSE FALSE FALSE
## Under15 FALSE FALSE FALSE
## FertilityRate FALSE FALSE FALSE
## ChildMortality FALSE FALSE FALSE
## .category TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 16 rows containing missing values (geom_point).
## Warning: Removed 16 rows containing missing values (geom_point).
## Warning: Removed 16 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## .category NA 1 NA <NA> 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## .category 0.5154639 TRUE TRUE NA
## [1] "numeric data missing in glbObsAll: "
## FertilityRate CellularSubscribers
## 22 20
## LiteracyRate GNI
## 182 64
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 186 186
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Country Region .lcn
## 0 0 0## [1] "glb_feats_df:"## [1] 18 12## id exclude.as.feat rsp_var
## LifeExpectancy LifeExpectancy TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## LifeExpectancy LifeExpectancy NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## LifeExpectancy NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## LifeExpectancy NA NA NA NA
## rsp_var
## LifeExpectancy TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 15 select.features 7 0 0 20.412 21.984 1.572
## 16 fit.models 8 0 0 21.984 NA NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 22.513 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 1 fit.models_0_bgn 1 0 setup 22.513
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet 22.549
## end elapsed
## 1 22.549 0.036
## 2 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: ChildMortality,CellularSubscribers.nonNA"
## [1] "myfit_mdl: setup complete: 1.032000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.171 on full training set
## [1] "myfit_mdl: train complete: 1.800000 secs"
## Length Class Mode
## a0 80 -none- numeric
## beta 160 dgCMatrix S4
## df 80 -none- numeric
## dim 2 -none- numeric
## lambda 80 -none- numeric
## dev.ratio 80 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) CellularSubscribers.nonNA
## 76.14190451 0.01563283
## ChildMortality
## -0.21034732
## [1] "max lambda < lambdaOpt:"
## (Intercept) CellularSubscribers.nonNA
## 76.18267877 0.01540236
## ChildMortality
## -0.21087483
## [1] "myfit_mdl: train diagnostics complete: 1.890000 secs"
## [1] "myfit_mdl: predict complete: 2.033000 secs"
## id feats
## 1 Max.cor.Y.rcv.1X1###glmnet ChildMortality,CellularSubscribers.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.762 0.011
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8565517 3.497785 0.8550496 0.8573102 3.488525
## max.Adj.R.sq.OOB
## 1 0.8558161
## [1] "myfit_mdl: exit: 2.038000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: ChildMortality,CellularSubscribers.nonNA"
## [1] "myfit_mdl: setup complete: 0.721000 secs"## Loading required package: rpart## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0104 on full training set
## [1] "myfit_mdl: train complete: 1.958000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 194
##
## CP nsplit rel error
## 1 0.70492837 0 1.0000000
## 2 0.08791502 1 0.2950716
## 3 0.07368671 2 0.2071566
## 4 0.01798421 3 0.1334699
## 5 0.01044633 4 0.1154857
##
## Variable importance
## ChildMortality CellularSubscribers.nonNA
## 68 32
##
## Node number 1: 194 observations, complexity param=0.7049284
## mean=70.01031, MSE=85.28855
## left son=2 (66 obs) right son=3 (128 obs)
## Primary splits:
## ChildMortality < 37.5 to the right, improve=0.7049284, (0 missing)
## CellularSubscribers.nonNA < 72.66 to the left, improve=0.4692647, (0 missing)
## Surrogate splits:
## CellularSubscribers.nonNA < 74.09 to the left, agree=0.856, adj=0.576, (0 split)
##
## Node number 2: 66 observations, complexity param=0.08791502
## mean=59.21212, MSE=36.43985
## left son=4 (25 obs) right son=5 (41 obs)
## Primary splits:
## ChildMortality < 87.2 to the right, improve=0.6048323, (0 missing)
## CellularSubscribers.nonNA < 64.64 to the left, improve=0.1155554, (0 missing)
##
## Node number 3: 128 observations, complexity param=0.07368671
## mean=75.57812, MSE=19.35327
## left son=6 (80 obs) right son=7 (48 obs)
## Primary splits:
## ChildMortality < 8.2 to the right, improve=0.4921724, (0 missing)
## CellularSubscribers.nonNA < 69.95 to the left, improve=0.1042004, (0 missing)
##
## Node number 4: 25 observations
## mean=53.2, MSE=10.08
##
## Node number 5: 41 observations
## mean=62.87805, MSE=17.03391
##
## Node number 6: 80 observations, complexity param=0.01798421
## mean=73.1875, MSE=10.72734
## left son=12 (14 obs) right son=13 (66 obs)
## Primary splits:
## ChildMortality < 25.85 to the right, improve=0.3467381, (0 missing)
## CellularSubscribers.nonNA < 69.95 to the left, improve=0.1285743, (0 missing)
## Surrogate splits:
## CellularSubscribers.nonNA < 51.2 to the left, agree=0.862, adj=0.214, (0 split)
##
## Node number 7: 48 observations
## mean=79.5625, MSE=8.329427
##
## Node number 12: 14 observations
## mean=69, MSE=6.857143
##
## Node number 13: 66 observations
## mean=74.07576, MSE=7.039715
##
## n= 194
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 194 16545.9800 70.01031
## 2) ChildMortality>=37.5 66 2405.0300 59.21212
## 4) ChildMortality>=87.2 25 252.0000 53.20000 *
## 5) ChildMortality< 87.2 41 698.3902 62.87805 *
## 3) ChildMortality< 37.5 128 2477.2190 75.57812
## 6) ChildMortality>=8.2 80 858.1875 73.18750
## 12) ChildMortality>=25.85 14 96.0000 69.00000 *
## 13) ChildMortality< 25.85 66 464.6212 74.07576 *
## 7) ChildMortality< 8.2 48 399.8125 79.56250 *
## [1] "myfit_mdl: train diagnostics complete: 2.645000 secs"
## [1] "myfit_mdl: predict complete: 2.673000 secs"
## id feats
## 1 Max.cor.Y##rcv#rpart ChildMortality,CellularSubscribers.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.234 0.01
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8845143 3.612457 NA 0.8845143 3.138409
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 NA 0.8507488 0.5005967 0.04168522
## [1] "myfit_mdl: exit: 2.682000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## bgn end elapsed
## 2 22.549 27.394 4.845
## 3 27.395 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: ChildMortality,CellularSubscribers.nonNA,ChildMortality:Under15,ChildMortality:ChildMortality"
## [1] "myfit_mdl: setup complete: 0.679000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00793 on full training set
## [1] "myfit_mdl: train complete: 2.244000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 3 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) CellularSubscribers.nonNA
## 77.421467560 0.012002399
## ChildMortality ChildMortality:Under15
## -0.335269381 0.002624827
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "CellularSubscribers.nonNA"
## [3] "ChildMortality" "ChildMortality:Under15"
## [1] "myfit_mdl: train diagnostics complete: 2.870000 secs"
## [1] "myfit_mdl: predict complete: 2.975000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 ChildMortality,CellularSubscribers.nonNA,ChildMortality:Under15,ChildMortality:ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.561 0.005
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.863628 3.508182 0.8614748 0.8641454 3.403945
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8620004 0.8648804 0.3711338 0.02530595
## [1] "myfit_mdl: exit: 2.984000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## 4 fit.models_0_Low.cor.X 1 3 glmnet
## bgn end elapsed
## 3 27.395 30.388 2.993
## 4 30.388 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality"
## [1] "myfit_mdl: setup complete: 0.699000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0368 on full training set
## [1] "myfit_mdl: train complete: 2.271000 secs"
## Length Class Mode
## a0 84 -none- numeric
## beta 1176 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 14 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos
## 76.694860465 -0.003728450
## .pos.y .rnorm
## -0.003378871 0.075193372
## CellularSubscribers.nonNA ChildMortality
## 0.006853132 -0.168446647
## FertilityRate.nonNA Over60
## 1.339044581 0.185942891
## Region.fctrAfrica Region.fctrAmericas
## -1.777056431 2.148315203
## Region.fctrEastern Mediterranean Region.fctrSouth-East Asia
## 1.868369727 0.709017555
## Region.fctrWestern Pacific Under15
## 1.469834756 -0.241038585
## [1] "max lambda < lambdaOpt:"
## (Intercept) .pos
## 7.672078e+01 -3.745903e-03
## .pos.y .rnorm
## -3.390183e-03 7.483258e-02
## CellularSubscribers.nonNA ChildMortality
## 6.838089e-03 -1.688486e-01
## FertilityRate.nonNA Over60
## 1.373235e+00 1.855136e-01
## Population Region.fctrAfrica
## -6.290707e-09 -1.763052e+00
## Region.fctrAmericas Region.fctrEastern Mediterranean
## 2.172714e+00 1.882474e+00
## Region.fctrSouth-East Asia Region.fctrWestern Pacific
## 7.393625e-01 1.490571e+00
## Under15
## -2.450473e-01
## [1] "myfit_mdl: train diagnostics complete: 2.847000 secs"
## [1] "myfit_mdl: predict complete: 2.956000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.566 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8998973 3.175766 0.892068 0.878346 3.221132
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8688312 0.889112 0.3435573 0.02328833
## [1] "myfit_mdl: exit: 2.964000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 4 fit.models_0_Low.cor.X 1 3 glmnet 30.388 33.366
## 5 fit.models_0_end 1 4 teardown 33.367 NA
## elapsed
## 4 2.978
## 5 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 21.984 33.376 11.392
## 17 fit.models 8 1 1 33.377 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 34.456 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 34.456 34.466
## 2 fit.models_1_All.X 1 1 setup 34.467 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 34.467 34.473
## 3 fit.models_1_All.X 1 2 glmnet 34.474 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality"
## [1] "myfit_mdl: setup complete: 0.692000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0368 on full training set
## [1] "myfit_mdl: train complete: 2.287000 secs"
## Length Class Mode
## a0 84 -none- numeric
## beta 1176 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 14 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos
## 76.694860465 -0.003728450
## .pos.y .rnorm
## -0.003378871 0.075193372
## CellularSubscribers.nonNA ChildMortality
## 0.006853132 -0.168446647
## FertilityRate.nonNA Over60
## 1.339044581 0.185942891
## Region.fctrAfrica Region.fctrAmericas
## -1.777056431 2.148315203
## Region.fctrEastern Mediterranean Region.fctrSouth-East Asia
## 1.868369727 0.709017555
## Region.fctrWestern Pacific Under15
## 1.469834756 -0.241038585
## [1] "max lambda < lambdaOpt:"
## (Intercept) .pos
## 7.672078e+01 -3.745903e-03
## .pos.y .rnorm
## -3.390183e-03 7.483258e-02
## CellularSubscribers.nonNA ChildMortality
## 6.838089e-03 -1.688486e-01
## FertilityRate.nonNA Over60
## 1.373235e+00 1.855136e-01
## Population Region.fctrAfrica
## -6.290707e-09 -1.763052e+00
## Region.fctrAmericas Region.fctrEastern Mediterranean
## 2.172714e+00 1.882474e+00
## Region.fctrSouth-East Asia Region.fctrWestern Pacific
## 7.393625e-01 1.490571e+00
## Under15
## -2.450473e-01
## [1] "myfit_mdl: train diagnostics complete: 2.952000 secs"
## [1] "myfit_mdl: predict complete: 3.065000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.59 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8998973 3.175766 0.892068 0.878346 3.221132
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8688312 0.889112 0.3435573 0.02328833
## [1] "myfit_mdl: exit: 3.073000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 34.474 37.552
## 4 fit.models_1_All.X 1 3 glm 37.553 NA
## elapsed
## 3 3.078
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality"
## [1] "myfit_mdl: setup complete: 0.676000 secs"
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 1.711000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.7469 -1.5068 0.1917 1.9945 6.7723
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.710e+01 2.598e+00 29.672 < 2e-16
## .pos -7.451e-03 4.013e-03 -1.856 0.06503
## .pos.y NA NA NA NA
## .rnorm 7.004e-02 2.264e-01 0.309 0.75744
## CellularSubscribers.nonNA 6.593e-03 7.296e-03 0.904 0.36742
## ChildMortality -1.732e-01 1.269e-02 -13.642 < 2e-16
## FertilityRate.nonNA 1.766e+00 5.648e-01 3.127 0.00206
## Over60 1.785e-01 7.245e-02 2.464 0.01467
## Population -1.733e-07 1.662e-06 -0.104 0.91711
## Region.fctrAfrica -1.609e+00 1.071e+00 -1.501 0.13498
## Region.fctrAmericas 2.449e+00 8.416e-01 2.909 0.00408
## `Region.fctrEastern Mediterranean` 2.027e+00 1.067e+00 1.900 0.05906
## `Region.fctrSouth-East Asia` 1.085e+00 1.216e+00 0.892 0.37349
## `Region.fctrWestern Pacific` 1.724e+00 8.998e-01 1.915 0.05702
## Under15 -2.931e-01 8.924e-02 -3.285 0.00123
##
## (Intercept) ***
## .pos .
## .pos.y
## .rnorm
## CellularSubscribers.nonNA
## ChildMortality ***
## FertilityRate.nonNA **
## Over60 *
## Population
## Region.fctrAfrica
## Region.fctrAmericas **
## `Region.fctrEastern Mediterranean` .
## `Region.fctrSouth-East Asia`
## `Region.fctrWestern Pacific` .
## Under15 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9.17023)
##
## Null deviance: 16546.0 on 193 degrees of freedom
## Residual deviance: 1650.6 on 180 degrees of freedom
## AIC: 995.91
##
## Number of Fisher Scoring iterations: 2
##
## [1] "myfit_mdl: train diagnostics complete: 2.404000 secs"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "myfit_mdl: predict complete: 2.455000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.03 0.01
## max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1 0.9002391 3.185692 995.914 0.8930342 0.8767941
## min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1 3.241612 0.8678959 0.888477 0.3501029
## max.RsquaredSD.fit
## 1 0.02351873
## [1] "myfit_mdl: exit: 2.464000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 37.553 40.026
## 5 fit.models_1_preProc 1 4 preProc 40.027 NA
## elapsed
## 4 2.473
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet ChildMortality,CellularSubscribers.nonNA
## Max.cor.Y##rcv#rpart ChildMortality,CellularSubscribers.nonNA
## Interact.High.cor.Y##rcv#glmnet ChildMortality,CellularSubscribers.nonNA,ChildMortality:Under15,ChildMortality:ChildMortality
## Low.cor.X##rcv#glmnet Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## All.X##rcv#glmnet Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## All.X##rcv#glm Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## max.nTuningRuns min.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 0 0.762
## Max.cor.Y##rcv#rpart 5 1.234
## Interact.High.cor.Y##rcv#glmnet 25 1.561
## Low.cor.X##rcv#glmnet 25 1.566
## All.X##rcv#glmnet 25 1.590
## All.X##rcv#glm 1 1.030
## min.elapsedtime.final max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0.011 0.8565517
## Max.cor.Y##rcv#rpart 0.010 0.8845143
## Interact.High.cor.Y##rcv#glmnet 0.005 0.8636280
## Low.cor.X##rcv#glmnet 0.007 0.8998973
## All.X##rcv#glmnet 0.006 0.8998973
## All.X##rcv#glm 0.010 0.9002391
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 3.497785 0.8550496 0.8573102
## Max.cor.Y##rcv#rpart 3.612457 NA 0.8845143
## Interact.High.cor.Y##rcv#glmnet 3.508182 0.8614748 0.8641454
## Low.cor.X##rcv#glmnet 3.175766 0.8920680 0.8783460
## All.X##rcv#glmnet 3.175766 0.8920680 0.8783460
## All.X##rcv#glm 3.185692 0.8930342 0.8767941
## min.RMSE.OOB max.Adj.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 3.488525 0.8558161
## Max.cor.Y##rcv#rpart 3.138409 NA
## Interact.High.cor.Y##rcv#glmnet 3.403945 0.8620004
## Low.cor.X##rcv#glmnet 3.221132 0.8688312
## All.X##rcv#glmnet 3.221132 0.8688312
## All.X##rcv#glm 3.241612 0.8678959
## max.Rsquared.fit min.RMSESD.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.8507488 0.5005967
## Interact.High.cor.Y##rcv#glmnet 0.8648804 0.3711338
## Low.cor.X##rcv#glmnet 0.8891120 0.3435573
## All.X##rcv#glmnet 0.8891120 0.3435573
## All.X##rcv#glm 0.8884770 0.3501029
## max.RsquaredSD.fit min.aic.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.04168522 NA
## Interact.High.cor.Y##rcv#glmnet 0.02530595 NA
## Low.cor.X##rcv#glmnet 0.02328833 NA
## All.X##rcv#glmnet 0.02328833 NA
## All.X##rcv#glm 0.02351873 995.914rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 40.027 40.079
## 6 fit.models_1_end 1 5 teardown 40.079 NA
## elapsed
## 5 0.052
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 33.377 40.086 6.709
## 18 fit.models 8 2 2 40.087 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 41.466 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## Max.cor.Y.rcv.1X1###glmnet ChildMortality,CellularSubscribers.nonNA
## Max.cor.Y##rcv#rpart ChildMortality,CellularSubscribers.nonNA
## Interact.High.cor.Y##rcv#glmnet ChildMortality,CellularSubscribers.nonNA,ChildMortality:Under15,ChildMortality:ChildMortality
## Low.cor.X##rcv#glmnet Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## All.X##rcv#glmnet Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## All.X##rcv#glm Over60,CellularSubscribers.nonNA,.rnorm,Population,Region.fctr,.pos,.pos.y,FertilityRate.nonNA,Under15,ChildMortality
## max.nTuningRuns max.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0 0.8565517
## Max.cor.Y##rcv#rpart 5 0.8845143
## Interact.High.cor.Y##rcv#glmnet 25 0.8636280
## Low.cor.X##rcv#glmnet 25 0.8998973
## All.X##rcv#glmnet 25 0.8998973
## All.X##rcv#glm 1 0.9002391
## max.Adj.R.sq.fit max.R.sq.OOB
## Max.cor.Y.rcv.1X1###glmnet 0.8550496 0.8573102
## Max.cor.Y##rcv#rpart NA 0.8845143
## Interact.High.cor.Y##rcv#glmnet 0.8614748 0.8641454
## Low.cor.X##rcv#glmnet 0.8920680 0.8783460
## All.X##rcv#glmnet 0.8920680 0.8783460
## All.X##rcv#glm 0.8930342 0.8767941
## max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet 0.8558161 NA
## Max.cor.Y##rcv#rpart NA 0.8507488
## Interact.High.cor.Y##rcv#glmnet 0.8620004 0.8648804
## Low.cor.X##rcv#glmnet 0.8688312 0.8891120
## All.X##rcv#glmnet 0.8688312 0.8891120
## All.X##rcv#glm 0.8678959 0.8884770
## inv.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 1.3123360
## Max.cor.Y##rcv#rpart 0.8103728
## Interact.High.cor.Y##rcv#glmnet 0.6406150
## Low.cor.X##rcv#glmnet 0.6385696
## All.X##rcv#glmnet 0.6289308
## All.X##rcv#glm 0.9708738
## inv.elapsedtime.final inv.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet 90.90909 0.2858952
## Max.cor.Y##rcv#rpart 100.00000 0.2768199
## Interact.High.cor.Y##rcv#glmnet 200.00000 0.2850479
## Low.cor.X##rcv#glmnet 142.85714 0.3148846
## All.X##rcv#glmnet 166.66667 0.3148846
## All.X##rcv#glm 100.00000 0.3139035
## inv.RMSE.OOB inv.aic.fit
## Max.cor.Y.rcv.1X1###glmnet 0.2866541 NA
## Max.cor.Y##rcv#rpart 0.3186328 NA
## Interact.High.cor.Y##rcv#glmnet 0.2937768 NA
## Low.cor.X##rcv#glmnet 0.3104499 NA
## All.X##rcv#glmnet 0.3104499 NA
## All.X##rcv#glm 0.3084885 0.001004103# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id min.RMSE.OOB max.R.sq.OOB
## 2 Max.cor.Y##rcv#rpart 3.138409 0.8845143
## 4 Low.cor.X##rcv#glmnet 3.221132 0.8783460
## 5 All.X##rcv#glmnet 3.221132 0.8783460
## 6 All.X##rcv#glm 3.241612 0.8767941
## 3 Interact.High.cor.Y##rcv#glmnet 3.403945 0.8641454
## 1 Max.cor.Y.rcv.1X1###glmnet 3.488525 0.8573102
## max.Adj.R.sq.fit min.RMSE.fit
## 2 NA 3.612457
## 4 0.8920680 3.175766
## 5 0.8920680 3.175766
## 6 0.8930342 3.185692
## 3 0.8614748 3.508182
## 1 0.8550496 3.497785# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fit + min.RMSE.fit
## <environment: 0x7fe9530af190>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Max.cor.Y##rcv#rpart"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 84 -none- numeric
## beta 1176 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 14 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos
## 76.694860465 -0.003728450
## .pos.y .rnorm
## -0.003378871 0.075193372
## CellularSubscribers.nonNA ChildMortality
## 0.006853132 -0.168446647
## FertilityRate.nonNA Over60
## 1.339044581 0.185942891
## Region.fctrAfrica Region.fctrAmericas
## -1.777056431 2.148315203
## Region.fctrEastern Mediterranean Region.fctrSouth-East Asia
## 1.868369727 0.709017555
## Region.fctrWestern Pacific Under15
## 1.469834756 -0.241038585
## [1] "max lambda < lambdaOpt:"
## (Intercept) .pos
## 7.672078e+01 -3.745903e-03
## .pos.y .rnorm
## -3.390183e-03 7.483258e-02
## CellularSubscribers.nonNA ChildMortality
## 6.838089e-03 -1.688486e-01
## FertilityRate.nonNA Over60
## 1.373235e+00 1.855136e-01
## Population Region.fctrAfrica
## -6.290707e-09 -1.763052e+00
## Region.fctrAmericas Region.fctrEastern Mediterranean
## 2.172714e+00 1.882474e+00
## Region.fctrSouth-East Asia Region.fctrWestern Pacific
## 7.393625e-01 1.490571e+00
## Under15
## -2.450473e-01## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Region.fctrAmericas 100.0000000 100.0000000
## Region.fctrEastern Mediterranean 86.7582580 86.7582580
## Region.fctrAfrica 81.7056463 81.7056463
## Region.fctrWestern Pacific 68.5378200 68.5378200
## FertilityRate.nonNA 62.8924472 62.8924472
## Region.fctrSouth-East Asia 33.6639577 33.6639577
## Under15 11.2575563 11.2575563
## Over60 8.5799966 8.5799966
## ChildMortality 7.7960960 7.7960960
## .rnorm 3.4641145 3.4641145
## CellularSubscribers.nonNA 0.3162482 0.3162482
## .pos 0.1728146 0.1728146
## .pos.y 0.1564780 0.1564780
## Population 0.0000000 0.0000000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 10
## Country Region Population Under15 Over60
## 348 Sierra Leone Africa 5979 41.74 4.41
## 292 Libya Eastern Mediterranean 6155 29.45 6.96
## 323 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 301 Marshall Islands Western Pacific 53 30.10 8.84
## 199 Angola Africa 20821 47.58 3.84
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 348 4.86 47 181.6 35.63
## 292 2.47 65 15.4 155.70
## 323 3.35 67 85.9 61.61
## 301 NA 60 37.9 NA
## 199 6.10 51 163.5 48.38
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 348 42.1 840 NA
## 292 89.2 NA NA
## 323 NA 2870 81.3
## 301 NA NA NA
## 199 70.1 5230 93.1
## PrimarySchoolEnrollmentFemale .src .rnorm .pos .pos.y .category
## 348 NA Test -0.4454942 348 348 .dummy
## 292 NA Test -0.4230342 292 292 .dummy
## 323 66.5 Test 1.5716339 323 323 .dummy
## 301 NA Test -1.2500570 301 301 .dummy
## 199 78.2 Test 0.7809128 199 199 .dummy
## Country.fctr Region.fctr FertilityRate.nonNA
## 348 Sierra Leone Africa 4.86
## 292 Libya Eastern Mediterranean 2.47
## 323 Pakistan Eastern Mediterranean 3.35
## 301 Marshall Islands Western Pacific 2.38
## 199 Angola Africa 6.10
## CellularSubscribers.nonNA LifeExpectancy.All.X..rcv.glmnet
## 348 35.63 39.30220
## 292 155.70 72.42347
## 323 61.61 59.73100
## 301 21.63 67.25795
## 199 48.38 43.75571
## LifeExpectancy.All.X..rcv.glmnet.err
## 348 7.697800
## 292 7.423472
## 323 7.268996
## 301 7.257954
## 199 7.244292
## LifeExpectancy.All.X..rcv.glmnet.err.abs
## 348 7.697800
## 292 7.423472
## 323 7.268996
## 301 7.257954
## 199 7.244292
## LifeExpectancy.All.X..rcv.glmnet.is.acc .label
## 348 FALSE Sierra Leone
## 292 FALSE Libya
## 323 FALSE Pakistan
## 301 FALSE Marshall Islands
## 199 FALSE Angola
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## .category .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## .dummy .dummy 194 194 194 1 1
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## .dummy 1 447.8162 2.308331 194
## err.abs.OOB.sum err.abs.OOB.mean
## .dummy 508.7739 2.622546
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 194.000000 194.000000 194.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 447.816227 2.308331
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 194.000000 508.773944 2.622546write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 47.747 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 18 fit.models 8 2 2 40.087 47.755 7.669
## 19 fit.models 8 3 3 47.756 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 19 fit.models 8 3 3 47.756 51.444
## 20 fit.data.training 9 0 0 51.445 NA
## elapsed
## 19 3.688
## 20 NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## Warning: Final model same as glb_sel_mdl_idrm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 0 0 51.445 51.873
## 21 fit.data.training 9 1 1 51.874 NA
## elapsed
## 20 0.428
## 21 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp imp
## Region.fctrAmericas 100.0000000 100.0000000
## Region.fctrEastern Mediterranean 86.7582580 86.7582580
## Region.fctrAfrica 81.7056463 81.7056463
## Region.fctrWestern Pacific 68.5378200 68.5378200
## FertilityRate.nonNA 62.8924472 62.8924472
## Region.fctrSouth-East Asia 33.6639577 33.6639577
## Under15 11.2575563 11.2575563
## Over60 8.5799966 8.5799966
## ChildMortality 7.7960960 7.7960960
## .rnorm 3.4641145 3.4641145
## CellularSubscribers.nonNA 0.3162482 0.3162482
## .pos 0.1728146 0.1728146
## .pos.y 0.1564780 0.1564780
## Population 0.0000000 0.0000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 10
## Country Region Population Under15 Over60
## 98 Libya Eastern Mediterranean 6155 29.45 6.96
## 107 Marshall Islands Western Pacific 53 30.10 8.84
## 142 Russian Federation Europe 143000 15.45 18.60
## 16 Belarus Europe 9405 15.10 19.31
## 166 Swaziland Africa 1231 38.05 5.34
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 98 2.47 65 15.4 155.70
## 107 NA 60 37.9 NA
## 142 1.51 69 10.3 179.31
## 16 1.47 71 5.2 111.88
## 166 3.48 50 79.7 63.70
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 98 89.2 NA NA
## 107 NA NA NA
## 142 99.6 20560 NA
## 16 NA 14460 NA
## 166 87.4 5930 NA
## PrimarySchoolEnrollmentFemale .src .rnorm .pos .pos.y
## 98 NA Train 0.2326315 98 98
## 107 NA Train -0.4578521 107 107
## 142 NA Train -0.7327977 142 142
## 16 NA Train 0.2941850 16 16
## 166 NA Train -0.3689090 166 166
## Country.fctr Region.fctr FertilityRate.nonNA
## 98 Libya Eastern Mediterranean 2.47
## 107 Marshall Islands Western Pacific 2.47
## 142 Russian Federation Europe 1.51
## 16 Belarus Europe 1.47
## 166 Swaziland Africa 3.48
## CellularSubscribers.nonNA .lcn .category
## 98 155.70 OOB .dummy
## 107 21.63 OOB .dummy
## 142 179.31 OOB .dummy
## 16 111.88 OOB .dummy
## 166 63.70 OOB .dummy
## LifeExpectancy.All.X..rcv.glmnet LifeExpectancy.All.X..rcv.glmnet.err
## 98 NA NA
## 107 NA NA
## 142 NA NA
## 16 NA NA
## 166 NA NA
## LifeExpectancy.All.X..rcv.glmnet.err.abs
## 98 NA
## 107 NA
## 142 NA
## 16 NA
## 166 NA
## LifeExpectancy.All.X..rcv.glmnet.is.acc
## 98 NA
## 107 NA
## 142 NA
## 16 NA
## 166 NA
## LifeExpectancy.Final.All.X..rcv.glmnet
## 98 73.85502
## 107 68.82223
## 142 76.87829
## 16 78.41524
## 166 57.18137
## LifeExpectancy.Final.All.X..rcv.glmnet.err
## 98 8.855020
## 107 8.822225
## 142 7.878286
## 16 7.415240
## 166 7.181373
## LifeExpectancy.Final.All.X..rcv.glmnet.err.abs
## 98 8.855020
## 107 8.822225
## 142 7.878286
## 16 7.415240
## 166 7.181373
## LifeExpectancy.Final.All.X..rcv.glmnet.is.acc .label
## 98 FALSE Libya
## 107 FALSE Marshall Islands
## 142 FALSE Russian Federation
## 16 FALSE Belarus
## 166 FALSE Swaziland
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "LifeExpectancy.Final.All.X..rcv.glmnet"
## [2] "LifeExpectancy.Final.All.X..rcv.glmnet.err"
## [3] "LifeExpectancy.Final.All.X..rcv.glmnet.err.abs"
## [4] "LifeExpectancy.Final.All.X..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 21 fit.data.training 9 1 1 51.874 56.316
## 22 predict.data.new 10 0 0 56.317 NA
## elapsed
## 21 4.442
## 22 NAStep 10.0: predict data new
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 10
## Country Region Population Under15 Over60
## 348 Sierra Leone Africa 5979 41.74 4.41
## 292 Libya Eastern Mediterranean 6155 29.45 6.96
## 323 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 301 Marshall Islands Western Pacific 53 30.10 8.84
## 199 Angola Africa 20821 47.58 3.84
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 348 4.86 47 181.6 35.63
## 292 2.47 65 15.4 155.70
## 323 3.35 67 85.9 61.61
## 301 NA 60 37.9 NA
## 199 6.10 51 163.5 48.38
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 348 42.1 840 NA
## 292 89.2 NA NA
## 323 NA 2870 81.3
## 301 NA NA NA
## 199 70.1 5230 93.1
## PrimarySchoolEnrollmentFemale .src .rnorm .pos .pos.y
## 348 NA Test -0.4454942 348 348
## 292 NA Test -0.4230342 292 292
## 323 66.5 Test 1.5716339 323 323
## 301 NA Test -1.2500570 301 301
## 199 78.2 Test 0.7809128 199 199
## Country.fctr Region.fctr FertilityRate.nonNA
## 348 Sierra Leone Africa 4.86
## 292 Libya Eastern Mediterranean 2.47
## 323 Pakistan Eastern Mediterranean 3.35
## 301 Marshall Islands Western Pacific 2.38
## 199 Angola Africa 6.10
## CellularSubscribers.nonNA .lcn .category
## 348 35.63 OOB .dummy
## 292 155.70 OOB .dummy
## 323 61.61 OOB .dummy
## 301 21.63 OOB .dummy
## 199 48.38 OOB .dummy
## LifeExpectancy.Final.All.X..rcv.glmnet
## 348 39.30220
## 292 72.42347
## 323 59.73100
## 301 67.25795
## 199 43.75571
## LifeExpectancy.Final.All.X..rcv.glmnet.err
## 348 7.697800
## 292 7.423472
## 323 7.268996
## 301 7.257954
## 199 7.244292
## LifeExpectancy.Final.All.X..rcv.glmnet.err.abs
## 348 7.697800
## 292 7.423472
## 323 7.268996
## 301 7.257954
## 199 7.244292
## LifeExpectancy.Final.All.X..rcv.glmnet.is.acc .label
## 348 FALSE Sierra Leone
## 292 FALSE Libya
## 323 FALSE Pakistan
## 301 FALSE Marshall Islands
## 199 FALSE Angola
## [1] TRUE## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"## [1] "glb_fin_mdl_id: Final.All.X##rcv#glmnet"## [1] "Cross Validation issues:"
## Max.cor.Y.rcv.1X1###glmnet
## 0## min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## Max.cor.Y##rcv#rpart 3.138409 0.8845143 NA
## Low.cor.X##rcv#glmnet 3.221132 0.8783460 0.8920680
## All.X##rcv#glmnet 3.221132 0.8783460 0.8920680
## All.X##rcv#glm 3.241612 0.8767941 0.8930342
## Interact.High.cor.Y##rcv#glmnet 3.403945 0.8641454 0.8614748
## Max.cor.Y.rcv.1X1###glmnet 3.488525 0.8573102 0.8550496
## min.RMSE.fit
## Max.cor.Y##rcv#rpart 3.612457
## Low.cor.X##rcv#glmnet 3.175766
## All.X##rcv#glmnet 3.175766
## All.X##rcv#glm 3.185692
## Interact.High.cor.Y##rcv#glmnet 3.508182
## Max.cor.Y.rcv.1X1###glmnet 3.497785## [1] "All.X##rcv#glmnet OOB RMSE: 3.2211"## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## .dummy 447.8162 508.7739 447.8162 508.7739
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.OOB .n.Tst
## .dummy 1 1 1 194 194 194
## .n.fit .n.new .n.trn err.abs.OOB.mean err.abs.fit.mean
## .dummy 194 194 194 2.622546 2.308331
## err.abs.new.mean err.abs.trn.mean
## .dummy 2.622546 2.308331
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 447.816227 508.773944 447.816227 508.773944
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 194.000000
## .n.OOB .n.Tst .n.fit .n.new
## 194.000000 194.000000 194.000000 194.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 194.000000 2.622546 2.308331 2.622546
## err.abs.trn.mean
## 2.308331## [1] "Final.All.X##rcv#glmnet prediction stats for glbObsNew:"
## id max.R.sq.new min.RMSE.new max.Adj.R.sq.new
## 1 All.X##rcv#glmnet 0.878346 3.221132 0.8688312## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Region.fctrAmericas 100.00000
## Region.fctrEastern Mediterranean 86.75826
## Region.fctrAfrica 81.70565
## Region.fctrWestern Pacific 68.53782
## FertilityRate.nonNA 62.89245
## Region.fctrSouth-East Asia 33.66396
## Under15 11.25756## [1] "glbObsNew prediction stats:"## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## label step_major step_minor label_minor bgn end
## 22 predict.data.new 10 0 0 56.317 67.441
## 23 display.session.info 11 0 0 67.442 NA
## elapsed
## 22 11.125
## 23 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 16 fit.models 8 0 0 21.984
## 22 predict.data.new 10 0 0 56.317
## 18 fit.models 8 2 2 40.087
## 2 inspect.data 2 0 0 7.591
## 17 fit.models 8 1 1 33.377
## 21 fit.data.training 9 1 1 51.874
## 19 fit.models 8 3 3 47.756
## 3 scrub.data 2 1 1 14.998
## 1 import.data 1 0 0 5.756
## 15 select.features 7 0 0 20.412
## 12 manage.missing.data 4 0 0 19.321
## 11 extract.features.end 3 6 6 18.453
## 20 fit.data.training 9 0 0 51.445
## 14 partition.data.training 6 0 0 20.253
## 10 extract.features.string 3 5 5 18.376
## 7 extract.features.image 3 2 2 18.239
## 9 extract.features.text 3 4 4 18.327
## 13 cluster.data 5 0 0 20.206
## 4 transform.data 2 2 2 18.144
## 6 extract.features.datetime 3 1 1 18.204
## 8 extract.features.price 3 3 3 18.291
## 5 extract.features 3 0 0 18.183
## end elapsed duration
## 16 33.376 11.392 11.392
## 22 67.441 11.125 11.124
## 18 47.755 7.669 7.668
## 2 14.998 7.407 7.407
## 17 40.086 6.709 6.709
## 21 56.316 4.442 4.442
## 19 51.444 3.688 3.688
## 3 18.144 3.146 3.146
## 1 7.591 1.835 1.835
## 15 21.984 1.572 1.572
## 12 20.206 0.885 0.885
## 11 19.321 0.868 0.868
## 20 51.873 0.428 0.428
## 14 20.411 0.158 0.158
## 10 18.452 0.076 0.076
## 7 18.290 0.051 0.051
## 9 18.376 0.049 0.049
## 13 20.252 0.047 0.046
## 4 18.183 0.039 0.039
## 6 18.239 0.035 0.035
## 8 18.326 0.035 0.035
## 5 18.203 0.020 0.020
## [1] "Total Elapsed Time: 67.441 secs"
## label step_major step_minor label_minor
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet
## 3 fit.models_0_Interact.High.cor.Y 1 2 glmnet
## 4 fit.models_0_Low.cor.X 1 3 glmnet
## 1 fit.models_0_bgn 1 0 setup
## bgn end elapsed duration
## 2 22.549 27.394 4.845 4.845
## 3 27.395 30.388 2.993 2.993
## 4 30.388 33.366 2.978 2.978
## 1 22.513 22.549 0.036 0.036
## [1] "Total Elapsed Time: 33.366 secs"